What the data for this category looks like
The documents covered in this category include property registration forms, asset appraisal reports, property transfer plans, on-site transaction announcements, and asset disposal approval documents. Data mainly comes from state-owned asset supervision information systems, enterprise asset management systems, property exchange systems, appraisal agency reports, and financial statements. The update frequency is monthly. Document fields cover asset ownership numbers, appraisal benchmark values, transaction approval status, disposal process nodes, and more. There are differences in format and field specifications across documents from different sources. Some documents include multi-page attachments and structured table content, with a character count range of 1000–50000 characters per single document.
What constraints these characteristics impose on model access and configuration
The monthly update frequency means the data synchronization cycle does not need to be too short. Data update trigger logic can be configured based on the monthly cycle. Documents from multiple sources with inconsistent formats require the model access layer to adapt to multiple text structures, which places requirements on context window adaptability. The wide character count range per single document requires configuring appropriate context and token limits to avoid processing timeouts or truncation of key ownership and appraisal information. The strict compliance requirements for state-owned asset disposal mean the model output must have high accuracy. Corresponding verification and fault-tolerance mechanisms must be configured to avoid compliance risks caused by model deviations.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long text content such as multi-page asset appraisal reports and property transfer plans, avoiding truncation of key information |
maxTokens | 2000–3000 token | Meets the output length requirements for summaries and verification results of declaration material reviews |
temperature | 0.1–0.3 | Reduces the randomness of model output, improving the accuracy of ownership verification and compliance checks |
quoteMaxToken | 1000–1500 token | Limits the maximum token count of referenced documents, avoiding interference from redundant information during reviews |
AIPROXY_API_ENDPOINT | Fill in the actual address of the deployed proxy service | Adapts to access requirements of the internal network environment of state-owned asset systems |
AIPROXY_API_TOKEN | Fill in the valid token generated by the proxy service | Ensures the identity legitimacy of model calls |
Three common misconfigurations
- After switching the large language model, the original model error is still prompted, and only the first configured model is read. This occurs because the default model was not updated in the model management interface of the FastGPT backend, and only the configuration file content was modified.
- Vector models deployed without a GPU environment cannot be added via OneAPI. This occurs because the call parameters of the vector model were not configured correctly, and the model loading logic for GPU-free environments was not adapted.
- The root account password for OneAPI is forgotten, making it impossible to manage model access configurations. This occurs because account credentials were not backed up in advance, and a standby process for password reset was not configured.
How to verify the configuration is successful
- Import a standard asset appraisal report, and check whether the model can correctly extract core fields such as asset ownership numbers and appraisal benchmark values.
- View the model call log to confirm that the requested API endpoint matches the configured
AIPROXY_API_ENDPOINT. - Adjust the value of the
temperatureparameter, observe changes in the randomness of the model output, and verify that the parameter takes effect. - Trigger a monthly data synchronization task, and confirm that the model can normally read updated state-owned asset system data.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.